library(tidyverse); library(cowplot)## Warning: package 'ggplot2' was built under R version 4.1.3
## Warning: package 'tibble' was built under R version 4.1.3
## Warning: package 'readr' was built under R version 4.1.2
library(phyloseq); library(decontam)
library(plotly); library(lubridate)## Warning: package 'lubridate' was built under R version 4.1.1
Run below to repeat ASV compilation from raw QIIME2 output.
# merged_tax <- read_delim("/Users/sarahhu/Desktop/Projects/microeuks_deepbiosphere_datamine/microeuk-amplicon-survey/data-input/taxonomy.tsv", delim = "\t")
# merged_asv <- read_delim("/Users/sarahhu/Desktop/Projects/microeuks_deepbiosphere_datamine/microeuk-amplicon-survey/data-input/microeuk-merged-asv-table.tsv", delim = "\t", skip = 1)
# metadata <- read.delim("/Users/sarahhu/Desktop/Projects/microeuks_deepbiosphere_datamine/microeuk-amplicon-survey/data-input/samplelist-metadata.txt")
#
# asv_wtax <- merged_asv %>%
# select(FeatureID = '#OTU ID', everything()) %>%
# pivot_longer(cols = !FeatureID,
# names_to = "SAMPLE", values_to = "value") %>%
# left_join(merged_tax, by = c("FeatureID" = "Feature ID")) %>%
# left_join(metadata) %>%
# filter(SITE == "GordaRidge" | SITE == "substrate" | SITE == "Laboratory") %>%
# filter(!grepl("Siders_", SAMPLE)) %>%
# filter(!(grepl("T0", SAMPLE))) %>%
# filter(!(grepl("T24", SAMPLE))) %>%
# filter(!(grepl("T36", SAMPLE))) %>%
# mutate(DATASET = case_when(
# grepl("_GR_", SAMPLE) ~ "GR",
# grepl("Gorda", SAMPLE) ~ "GR",
# grepl("_MCR_", SAMPLE) ~ "MCR",
# grepl("Axial", SAMPLE) ~ "Axial",
# TRUE ~ "Control or blank")) %>%
# separate(Taxon, c("Domain", "Supergroup",
# "Phylum", "Class", "Order",
# "Family", "Genus", "Species"), sep = ";", remove = FALSE)
#
# # fix naming, some controls sequenced separately.
# gr_substrate_fluid_asvs <- asv_wtax %>%
# mutate(SAMPLE_tmp = case_when(
# Sample_actual == "" ~ SAMPLE,
# TRUE ~ Sample_actual
# )) %>%
# select(-SAMPLE) %>%
# select(SAMPLE = SAMPLE_tmp, everything()) %>%
# filter(value > 0)
# View(gr_substrate_fluid_asvs)
# View(gr_substrate_fluid_asvs %>% filter(Sample_or_Control == "Control") %>% select(SAMPLE) %>% distinct())
# save(gr_substrate_fluid_asvs, file = "/Users/sarahhu/Desktop/Projects/GordaRidge-microcolonizers/microcolonizers-GordaRidge/input-data/asv-table.RData")Microcolonizers (or ‘cones’) were deployed at the Gorda Ridge hydrothermal vent field. Each microcolonizer was placed over a region of visible diffuse fluid flow. A total of 6 microcolonizers were depeloyed at one time, pairs of experiments were picked up after 6, 7, and 8 days.
Each microcolonizer chamber had 6 different substrates, so that diffuse fluid could reach each substrate. Temperature loggers also recorded temperature for the duration of the deployments. Substrates included: shell, riftia shell, quartz, pyrite, basalt, and olivine.
Microcolonizers at Mt. Edwards vent site. Credit: Ocean Exploration Trust
Recovering microcolonizers with ROV Hercules. Credit: Ocean Exploration Trust
Upon recovery of each experiment, substrates were saved for microscopy and molecular analysis. For the sequence data below (shell, quartz, and riftia), RNA was extracted, cDNA was created and the V4 18S rRNA hypervariable region was amplified and sequenced. Blank substrates (which sat with milliQ during the shipboard processing) were also sequenced alongside the experimental treatments.